Meeting AI: The Complete FAQ Guide

Meeting AI converts project meetings into searchable records and actionable documents. Meeting AI helps project teams document conversations for future reference within project folders. Teams can input transcriptions of meetings through our AI or upload transcripts of meetings from other applications like Zoom, Teams, Meet, etc.

After the AI reads the transcription text, it is able to draft meeting notes, also known as Minutes of Meeting. Information gets auto-archived to the relevant project folder. Users can search and ask questions about what was discussed, decided, or assigned and get answers based on the actual meeting content.

Construction project requirements and tasks evolve at the speed of light, making hand-logging almost impossible. Blurry call notes and attendance records show that a call was held; the plumbing reroute was either approved or discussed, but after two weeks, participants are unsure about the outcome.

Meeting AI keeps that history retrievable. Instead of looking through old notes or having to rewatch meetings, you can now get answers through direct questions.

Meeting AI is meant for construction teams who need a way to simplify capturing, organizing, and retrieving meeting information. It is valuable for project managers who need to manage the oversight of weekly coordination calls, as well as for VDC teams who need to manage BIM coordination decisions throughout multiple meetings.

For superintendents, it means they no longer have to stop their fieldwork to dig through notes to find the information they need to manage the coordination meetings, and it helps the coordination meeting participants manage OAC meetings and trade calls as well as internal reviews. In general, if you find yourself asking “Wait, what did we decide about that two meetings ago?”, then Meeting AI will help you find the answer.

Weekly coordination meetings, OAC meetings, BIM coordination sessions, progress reviews, internal team check-ins, essentially any recurring or one-off project meeting that gets recorded or uploaded. Each type can live in its own dedicated meeting room within a project, keeping related discussions grouped. There's no fixed list of "supported" meeting types beyond that. If it's a project meeting and there's a recording, it can be processed.

Broadly, a tool that processes a meeting recording and generates some combination of a transcript, summary, and notes automatically, removing the need for someone to manually take notes throughout. General-purpose versions, Otter and Fireflies are common examples, work across any kind of meeting: sales calls, standups, interviews. Construction-specific versions narrow that scope deliberately, built around the vocabulary, meeting types, and coordination patterns unique to construction projects.

Meeting assistant AI’s are able to transcribe and summarize meetings, but most likely will not understand construction-specific terms such as RFIs, Submittals, coordination clashes, BIM conflicts, and BIM decisions. A construction-specific meeting AI is constructed with this terminology and can comprehend how construction teams structure their meetings, be it an OAC meeting, a weekly coordination call, a trade meeting, or a BIM meeting.

This context applies specifically to what happens after the meeting. Action items can be generated and shown within the context of the construction process, decisions can be captured within the scope of the project team discussions, and information can be retrieved to answer project-related meeting questions.

Meeting AI is more than a transcription or minutes generator tool. The first step is always transcription. Capturing what was said in a meeting is important, but not the main value-add. The next step is generating Minutes of Meeting (MOMs), which converts a conversation into documented, structured information.

The main value is having a record that is searchable and answerable to queries, not only for a single meeting, but also for an entire project room. Compared to five weeks ago, imagine not having to open a meeting recording, scroll through a transcript, and attempt to locate the meeting discussion. Instead, you can just ask what was decided or what action was assigned.

It replaces the old record-taking, notes, manual search, follow-up cycle with something shorter: upload, process, ask, retrieve. The meeting still happens the same way it always did. What changes is everything after it, since the searching and remembering get handled automatically instead of falling on whoever happened to take notes that day.

By asking a direct question instead of searching through notes or rewatching a recording. Something like "what was decided about the plumbing reroute" pulls the answer straight from whichever meeting covered it, without anyone needing to remember which specific call that was. This works whether you're asking about one specific meeting or searching across every meeting inside a project's meeting room.

Meeting AI allows you to skip looking through the recording or transcripts to find the answer to a question. Instead, you can ask what was discussed and get a relevant answer in a few seconds. This can save a lot of time on a project with multiple months of weekly meetings. Old recordings can be deleted or stored instead of being constantly revisited. The meeting information can be retrieved when needed.

Meeting AI can help review what happened in the last meeting. For the next meeting, a team can check the previous meeting summary or action items and be prepared to follow up on what is still open. This is very useful for coordination calls and OAC sessions where the team can be informed about what was decided or resolved in the previous meetings.

Yes, teams can upload meeting recordings. You are not required to run meetings through our platform to capture recordings. There are two ways to capture meeting recordings. One way is to use our native recording feature, and the other way is to upload a recording file. Both ways use the same processing pipeline. This is important for teams that use a specific meeting platform and are not willing to change their meeting platform for a meeting intelligence tool.

The recording and the transcript are processed by the system to identify the meeting agenda, discussion points, decisions, action items, and any important issues. From there, a meeting summary and a set of Minutes of Meeting are created. Once that's done, the meeting becomes part of the searchable record. You can question it directly, on its own or alongside every other meeting in that project's room.

AI technology summarizes audio recordings and meeting notes into outlined action items and discussion points, and it fills in the required portions of the MOM template without the necessity of human input to transcribe and create the MOM. What used to be a task someone did after the meeting, often imperfectly, from memory and quick notes, happens automatically from the actual conversation.

The output still gets reviewed before it's treated as final. But the first draft, the part that used to eat real time, is already done.

It finds language in the transcript that assigns a task, responsibility, or follow-up to a specific person. It does not pick up action items in the same way that general notes would. For example, it would pick up that "I'll get that drawing to you by Friday" is an action item for the person who said it and would not get lost in the conversation as it might in a set of rough handwritten notes.

It then includes action items in the MOM it generates. Because of this, action items are placed in a distinct section rather than in the discussion notes.

Meeting AI documents decisions in an easily searchable format rather than in the general flow of the discussion. This way, I can determine if the plumbing reroute was approved without having to recall which meeting it was decided in and then have to listen to the notes or the recording of that meeting.

For a project that spans many months and contains numerous repetitive meetings, this decision document creates a traceability of what was discussed and decided, simplifying the task of tracking project decisions.

By automating the parts that used to require someone's dedicated time after every single meeting: writing up notes, drafting a MOM, tracking down who agreed to what. That administrative overhead adds up fast across a project running several recurring meetings a week. Less time on write-up work means more time spent on the coordination the meetings exist for in the first place.

Attendees and their roles. The date, project, and meeting type. A summary of what was discussed, organized by agenda item. Decisions made, stated clearly enough that there's no ambiguity later about what was agreed. Action items with names and target dates attached. Open issues carried forward to the next meeting. Skipping any step makes the document not useful when someone has to find a reference for it weeks later.

AI-generated MOMs are more complete than manually typed minutes because AI-generated minutes are transcribed directly, and manually typed minutes are dependent on how well a person can transcribe as they participate in a discussion. Manual minutes usually contain things that are missed because a person attempts to listen, participate, and transcribe at the same time. AI MOM’s require a human review because context and nuance can be misread.

Because they usually get assigned verbally, in passing, buried inside a longer discussion, and whoever's taking notes that day may or may not catch every one. Add multiple meetings a week across multiple projects, and even a diligent note-taker starts missing things simply from volume.

Without a system specifically built to catch and preserve these commitments, they rely entirely on memory and handwritten notes, which is a fragile combination over the length of a real project.

A single running list, not scattered notes from each meeting. Every new action item gets added to that list with its source meeting, assigned owner, and target date, and items get marked resolved as they close out instead of just fading from memory once the meeting that raised them is a few weeks old.

Meeting AI supports this directly by identifying action items automatically from each meeting's transcript, feeding a consistent record instead of relying on someone to manually compile one after every session.

By treating decisions as their own category, separate from general discussion notes, and keeping them searchable instead of buried in whichever meeting they happened to come up in. A decision made in week three shouldn't require remembering it was week three just to find it again in week twelve.

A searchable meeting record solves this directly, since a decision made months ago is just as retrievable as one made yesterday.

Work sometimes proceeds based on an assumption that turns out to be wrong, or a decision gets re-litigated because nobody remembers it was already settled weeks earlier. Both waste real time, and the second one can create real friction when someone insists a decision went one way and nobody can actually confirm it either way.

Multiply that across a project running months of recurring meetings, and forgotten discussions become a meaningful, if often invisible, source of rework and wasted coordination time.

By keeping every processed meeting permanently searchable instead of letting it fade into an old recording nobody has time to revisit. Information loss on a construction project usually isn't dramatic. It's gradual, a detail here, a decision there, quietly slipping out of anyone's active memory as weeks pass.

A searchable record stops that slow leak. The information's still there, exactly where it was discussed, whenever someone actually needs it again.

A continuously updated list of open action items that carries forward from meeting to meeting, instead of starting fresh each time with no memory of what's still outstanding from before. New items get added as they come up. Resolved items get marked closed. Nothing quietly falls off the list just because a few weeks passed.

Meeting AI supports this by identifying action items automatically from every processed meeting, so the rolling list builds itself from actual meeting content instead of depending on someone manually compiling it after each session.

Specific, direct ones. "What was discussed about Level 2 coordination?" "What action items were assigned to HVAC?" "Which meeting discussed ceiling clearance issues?" "Was the plumbing reroute approved?" Each of these gets answered directly from the actual meeting content, not returned as a pile of transcript excerpts to sift through manually.

The common thread: these are exactly the questions people already ask each other in hallway conversations, just answered instantly instead of requiring someone to remember and explain it themselves.

Meeting AI uses dedicated meeting rooms for each project or topic, where processed meetings are grouped. When you ask a question at the room level, the system searches across all the meetings in that room rather than only the meeting currently open.

This makes questions like, “Which meeting discussed ceiling clearance issues?” easy to answer. You don’t need to remember which meeting covered the topic or search through recordings one by one. Meeting AI searches the entire meeting history in that room and surfaces the relevant discussion.

AI allows team discussions to be documented as part of the project record. AI records team conversations, ensuring that context is retained when a project team member's role transitions or when the member leaves the project entirely.

Saving meeting knowledge in this way means that the person who needs context does not have to try to follow the person who attended the meeting and therefore possesses the context.

Teams can search by topic instead of trying to remember which specific meeting covered it. Because processed meetings remain searchable in the project room, a discussion from three months ago can be retrieved just as easily as one from yesterday. There’s no need to scroll through a calendar, guess the meeting date, and then search through the recording.

This is a practical advantage over a basic recording archive, where finding an old discussion often starts with knowing roughly when the conversation took place.

When participants say something like, “Was this approved?” or “What was the decision?” it is quick to check the searchable meeting record, which might take ten to fifteen seconds. If the record is not searchable, then it could be a round of emails or many minutes spent asking around to everyone on the distribution list.

Time is of the essence in this case. A time critical, wait on reply, field decision should not be delayed while someone tries to find three weeks’ worth of notes.

Information related to meetings is left in recordings of video calls that require viewing again. Notes that were not typed. Jottings and memories that fade. None of the formats described offer quick, efficient, or precise navigation to a particular conversation or decision. Structured information that can be easily searched reduces the friction of having to search through a lot of redundant information to gather context.

Yes, and that's a meaningful difference from a plain transcription tool. A transcript dump still requires someone to read through it looking for the relevant part. Meeting AI answers the actual question directly, pulling the specific information from wherever it appears in the meeting content. Ask "was the plumbing reroute approved," and you get a direct answer, not a searchable text file you still have to comb through yourself.

Keeping BIM coordination meetings searchable across weeks or months of ongoing model coordination. A clash discussed and resolved five sessions ago is easy to lose track of otherwise, especially on a project running weekly coordination calls for the length of an entire construction phase.

Ask which meeting covered a specific coordination issue, and the answer comes back instantly, instead of requiring someone to guess which of a dozen past sessions covered it.

Asking directly about the specific system, area, or clash in question. "What was decided about the mechanical routing on Level 3" pulls the relevant discussion straight from whichever coordination session actually covered it, without anyone needing to remember which specific week that conversation happened.

Given how many coordination sessions a complex project can run over its lifecycle, this kind of direct retrieval matters a lot more than it might seem on a smaller job with just a handful of meetings total.

Meeting AI simplifies the administrative work of creating minutes, tracking action items, and compiling the decisions made in multiple meetings that happen regularly. It also remedies the need to search for details of past meetings through notes or recordings when answering stakeholder requests for meeting updates. For project managers who are continuously juggling several projects, the searchable meeting logs provide a great value and eliminate the need to capture critical details in their memory.

Meeting AI provides superintendents with quick answers, eliminating the need to leave fieldwork to review notes or call someone at the office. An example question, “Did we address the ceiling clearance issue in that mechanical room?”, can now be answered, allowing superintendents to keep moving rather than spending field time to collect information that is recorded in a previous meeting.

This technology provides a great opportunity for superintendents who deal with multiple field-based responsibilities. Superintendents can now manage their field sites more efficiently throughout the workday because less time is spent searching for repeated information.

Coordination meetings occur often and are cumulative in nature. Each meeting builds off of previously documented decisions and open items. Losing this history will result in needless discussions to resolve unresolved items. Meeting AI eliminates this by making all coordination meeting decisions and open items searchable across the entire project.

Being able to ask what was resolved in a prior session, instead of relying on someone's memory of a meeting from weeks back, keeps that cumulative coordination process actually coherent over time.

Using context from previous meetings rather than just specs and drawings to create an estimate is probably more efficient. One number in an estimate can change multiple times because of a coordination meeting decision (for example, someone add a new scope item), an item in a requirement being clarified, etc. Easily lost context if it solely exists in someone’s meeting notes from several weeks ago.

Searchable meeting history means an estimator can check what was discussed and decided instead of working from an incomplete picture.

Owner-Architect-Contractor meetings bring together the three key project stakeholders to review status, address concerns, and make decisions that are generally time-sensitive. OAC meetings provide an opportunity to establish project consensus and are considered some of the most critical meetings on a project because they address cross-stakeholder concerns, not just cross-functional concerns within one organization.

Given the number of parties and the weight of what usually gets decided there, OAC meetings are exactly the kind of session where losing track of a decision creates real, sometimes costly, downstream confusion.

The overall project schedule, progress and updates, open Requests for Information (RFI) status, change orders, budgets and costs, and design, scope, or architectural input requests are typically covered in OAC meetings. During these meetings, teams can discuss milestones, project risks, and issues that may impact the budget or overall project schedule. Because OAC meetings tend to cover a wide range of components of a project and may include a variety of tasks, they may also include the making of an important, yet easily lost decision if it is not documented.

A BIM coordination meeting is a coordination session for teams to review 3D models and resolve clashes between systems at the design stage rather than allow these to become issues in the field. The design teams are better able to deal with conflicts in their systems in the design 3D model than in the field.

Since these clashes may be resolved by a quick update of the 3D model, finding clashes in BIM coordination meetings may avoid delays and additional costs to the project. However, finding these clashes in the field may result in the same.

A coordination meeting ensures that different systems and scopes integrate without conflicting. A progress meeting checks on the status and schedule of the work, determining if things are on track, what is next, and what risks are present. While they do overlap somewhat, each is addressing a different core question. One is concerned with if things integrate. The other is if we are on schedule.

Because different questions need different rooms and different participants. Coordination issues need the trades involved in the conflict. Owner-level decisions need the owner and architect at the table. Internal team check-ins need just the people directly executing the work, without pulling in every external party for something that's purely internal.

Fitting all into one meeting type would stretch the agenda or neglect the individuals who had a say in the matter.

Yes. Internal team check-ins, progress reviews, and planning sessions all process the same way as an OAC or coordination meeting, generating a summary, a MOM, and a searchable record either way. The distinction between internal and external meetings matters for who's in the room, not for how the tool processes what got discussed.

Handling transcription, note-taking, minute-drafting, and action item recording is repetitive work AI can assist with for construction meetings. This allows participants to focus on the real purpose of meetings, i.e., discussions and decision-making, rather than on note-taking.

AI will create records of individual meetings, and even entire sessions, building a searchable database. Maintaining the history of a project manually will be nearly impossible as individual meetings will be conducted every day by different teams.

Estimating the time savings from using an AI meeting assistant is case-dependent; however, significant time savings from manual note-taking and searching are almost guaranteed. Cutting out manual minutes drafting alone saves real time after every single meeting.

Cutting out the time spent hunting for old information saves even more, especially on projects running months of recurring coordination calls.

There's no universal figure that applies to every team, but the two biggest sources of savings, less write-up time and faster information retrieval, both scale directly with how meeting-heavy a project actually is.

Meeting AI helps users by producing a summary and/or minutes of the meeting. This technology helps because no one has to type notes during a meeting, thus increasing distraction and cause important details to slip through the cracks. Not having to take notes helps participants focus on the contents of the meeting and the decisions made.

There are a lot of small indirect consequences of inadequate meeting notes. For example, there are a lot of time-wasting re-discussions because there is no record of the discussed decisions. In addition, there are forgotten and undocumented tasks due to the lack of meeting notes. There are also unresolved disputes because of the absence of a meeting record. All of these consequences have no direct impact on a project budget.

Rather, there are time and cost impacts due to the inefficiencies caused by the inadequate meeting notes. In addition, some disputes could be avoided by the provision of a meeting record.

Before the availability of recorded meetings, onboarding new project employees would require other staff to explain their roles and the decisions associated with the project. Meeting AI has eliminated the time-consuming onboarding process and made it simple to transfer knowledge of the project decisions by providing the meeting notes for each stage of the project. There is no longer a need to remember history because the record is now searchable. Each event in the room will remain documented regardless of the participant’s presence or absence.

Yes, since it creates an actual record of what was discussed and decided, instead of leaving it down to competing memories of who said what. A dispute over whether something was approved in a meeting becomes far easier to settle when there's a searchable record to check, rather than two people simply disagreeing with no way to verify either version.

This doesn't eliminate disputes, but it removes a common and avoidable category: disagreements over what happened that a clear record would settle in seconds.

Otter.ai and Fireflies.ai are other popular general-purpose AI meeting assistants that provide features such as transcription, summarization, and automation of meeting notes. Zoom and Microsoft Teams provide native AI meeting features in their respective platforms too. These can be adequate for most business meetings, though there are no specific features for the construction domain, like meeting and task allocation in a project coordination flow.

iFieldSmart Meeting AI is designed for the construction domain. Meetings can be recorded and converted to action- and decision-related notes and meeting minutes.

iFieldSmart Meeting AI is built specifically for construction teams, with an understanding of construction terminology, meeting types, and project coordination workflows. The main difference is context. Otter and Fireflies can handle any type of meeting by automatically summarizing and transcribing meeting conversations. On the other hand, iFieldSmart Meeting AI is particular to construction meetings, with a focus on RFIs, submittals, and coordination clashes.

While treating every recording as another generic meeting event, Meeting AI also organizes meetings by creating project-specific rooms for weekly coordination meetings, OAC meetings, BIM meetings, etc.

For construction teams, an AI meeting assistant must understand terminology and concepts around RFIs, submittals, and coordination clashes. It must provide the ability to organize meetings by project and type. It also needs the ability to perform an intelligent, cross-meeting search by project, which enables users to find information, rather than having to search individual meeting transcripts.

It should also provide tools that enable project teams to efficiently track actions and decisions that carry over the course of the many meetings a project spans, where decisions require a lot of context and build upon previous team discussions.

Meeting assistants can recognize a lot of construction terms; however, they are not focused on construction terminology and workflows. Industry-specific terms such as RFIs, submittals, and coordination clashes may not be understood with the context a construction team would expect.

This is an issue when understanding a term means more than just knowing how to spell it correctly. For example, search requests, summaries, action items, and decisions could all depend on the understanding of the term in the context of the project. iFieldSmart Meeting AI was designed with the context of construction terminology and meetings in mind.

No. The majority of general AI meeting note-takers do not connect meetings with project information like RFIs, submittals, drawings, or specifications. Although these assistants may tell you what was said during the meeting, they typically do not connect the conversation to the project information.

That gap is where a construction-specific tool can add value by keeping meeting information connected to the project context teams need to reference and act on later.

A notetaker's job ends at transcription and summary. A construction meeting intelligence platform goes further, connecting meetings to project context, organizing them into searchable rooms by project and meeting type, and answering direct questions across the full history of what's been discussed. One documents a meeting. The other turns a whole series of meetings into something you can query like a living project record.

Free tiers are great for very basic tasks, but the length and feature limits make them very suitable to only internal notes and casual transcriptions. Beyond that, the free tiers lack safety and reliability features and should not be a consideration for serious projects that involve contracts or talking points that could impact client negotiations. The limitations become apparent rather quickly.

The implementation varies from vendor to vendor, but the enterprise level of automation should offer some level of data encryption for stored meeting recordings and transcripts, set data retention standards, and offer some level of access control for meeting content. You must confirm these vendors' specifics before purchasing, as you should not expect them to meet a certain general standard.

Given how much sensitive project and contractual discussion happens in construction meetings, this is worth real scrutiny, not a quick skim of a privacy policy page.

Software specifically built to capture, organize, and store meeting minutes for construction projects, often including features like action item tracking, RFI linkage, and searchable historical records across many meetings over the life of a project. It's a narrower category than general meeting software, purpose-built around construction's specific documentation needs. Some tools in this category also add AI-generated transcription and summarization on top of the core minutes-tracking functionality.

Structured minutes templates matching construction documentation standards. Action item tracking with assigned owners and due dates. Connection to project data like RFIs and submittals. Search across historical meetings. Distribution tools designed to get meeting minutes to the appropriate recipients as quickly as possible after the meeting ends. There are differences in features across vendors. However, there are some tool categories that show up consistently throughout most of the tools built for this purpose.

Traditional general templates offer a blank structure and headers for attendees, agenda, actions/tasks, but still require someone to enter all of the information after every meeting. Construction-related software helps enter, or even fill, information from a recording or a transcript of the meeting and associates it with actual project data, not "raw," disconnected text. The template answers what should go in a document. The software gets it there without someone typing it all out by hand.

Some do, with varying degrees of depth depending on the specific tool and how it's built. A meeting discussing a specific RFI might get automatically linked to that RFI's record, or an identified action item might feed directly into a project's broader action item tracking system.

The level of automatic connection varies a lot across the market, so confirming exactly what a specific tool supports matters more than assuming based on general category expectations.

Manual note-taking in Word or Excel templates, still surprisingly common on smaller projects. General-purpose AI meeting assistants like Otter or Fireflies, used without any construction-specific customization. Simply relying on memory and follow-up emails, which is more common than it probably should be, especially on smaller teams.

Each of these works up to a point. None of them scale particularly well once a project starts running dozens of meetings across multiple recurring types.

Because a template only provides structure. It doesn't search, doesn't connect to project data, and doesn't help anyone find a specific decision made three months and forty meetings ago. On a complex project running multiple recurring meeting types every week, that manual structure turns into a growing pile of disconnected documents nobody has time to search through effectively.

It works fine for a small project with a handful of meetings total. It struggles hard once volume and project complexity climb.

It would likely be legal to record a meeting with an AI notetaker; however, it would depend on the location of the meeting and who is present, since consent-to-record laws do differ by jurisdiction. Some places only require one party to consent to a recording. Others require every participant's consent before recording is legal at all.

This is genuinely a legal question worth confirming with counsel familiar with your specific jurisdiction, not something to assume based on general practice elsewhere.

In some locations, one-party consent recording is lawful; here it’s only necessary that one of the meeting participants thinks recording is a good idea. In other locations, it is required that all the meeting participants agree for recording to happen. Given the important differences and significant negative impacts of getting this wrong, ensuring the correct mandate for your location isn’t a complete waste of time.

Some important aspects include the storage and accessibility of the recordings and transcriptions, their duration of storage, and whether the data is being used to train models for other clients or projects. Construction meetings can contain financial information, as well as other sensitive information relating to contracts and designs, so this should be addressed properly and not just with a quick read of a privacy policy.

It should be explicitly stated in the platform’s contract or terms of use, and it is worth reviewing closely. Ownership can vary significantly; sometimes the contracting organization, sometimes the platform, sometimes joint ownership. It relies heavily on how the contract is worded. The importance of documenting meeting logs in potential future conflicts makes this an important issue to document now.

It is best to proactively formulate a policy to address recording consent in the meeting jurisdictions applicable to the organization and to assign consent and capture policy to external stakeholders, the duration of retention, and the permitted and prohibited policy uses. This may greatly minimize challenges that will later define the legal and compliance boundaries of meeting recordings.

The absence of a corporate recording and consent policy will not result in the complete absence of legal and record retention compliance challenges for the organization. It will, however, result in additional costs to the organization to address such challenges in an adverse and less amenable legal and business environment.

A clear meeting agenda should be sent to all required parties to prepare them, rather than having them show up unprepared, and to minimize the need for a lengthy discussion. The main points should be addressed, closure should be recorded, and a meeting of the minds should be documented. Minutes should be quickly drafted to capture the key messages and concrete decisions reached.

Distribute minutes promptly afterward, while the discussion's still fresh in everyone's mind. Because of the number of stakeholders involved and the importance of the decisions made during OACs, poorly documented OACs create more issues down the line for almost all other meeting types.

Review the combined model together, rather than just addressing issues verbally and abstractly. Clashes should have specific owners assigned to them; the resolution shouldn't be the responsibility of “someone” who is going to "look into it." Track resolution status meeting to meeting so nothing quietly falls off the list.

Coordination meetings are cumulative by nature, building on prior sessions, so losing track of where a specific issue stands creates real confusion fast.

In constructing a meeting agenda, priority needs to be placed on items since the final meeting always runs out of time. The item’s duration on the agenda needs to be included. Agendas must contain the presenter of the meeting item or the lead of the item and must outline follow-up tasks from the previous meeting. This ensures that no items end up unaddressed. Unstructured meeting agendas with no ownership generally result in agendas that are unproductive, and time is wasted.

Constantly setting a meeting agenda and sticking to it can prevent meeting creep (meetings funneled into unstructured status check meetings that don’t culminate in any action). If a meeting is scheduled with no decision to make, it should either be shorter or occur rarely, or even better, replaced with a meeting that helps drive a decisive action. Tracking the results of recent meetings and using a record of meetings that is searchable will show an unproductive pattern of meetings that produce no outcome or action.

Because the longer minutes take to circulate, the more likely someone's memory of what happened starts drifting from what actually got decided. Prompt distribution catches misunderstandings while the meeting's still fresh in everyone's mind, when it's cheap and easy to correct a misread, not weeks later once it's turned into a real disagreement.

It also gives people a chance to flag anything inaccurate quickly, while correcting it is still simple, instead of after it's already been acted on based on a mistaken understanding.

By searching patterns that are difficult to identify without access to a database, we find out which meetings result in decisions, which meetings tend to run long without having a lot resolved, and which action items are persistent and not getting closed.

That kind of visibility helps a team refine how they run meetings over time, based on actual patterns in what's happened, not gut feeling about which sessions feel productive.

Accuracy depends heavily on whether the underlying model's been built or tuned with construction vocabulary specifically in mind. General-purpose transcription tools struggle to analyze content in noisy environments. That includes an environment with background noise or crosstalk. Tools intended for construction analysis tend to outperform general-purpose tools because they’re designed for this vocabulary. General-purpose tools try to adapt afterward.

Yes, always, the same way any first draft benefits from a second look before it goes out. Transcription and interpretation aren't perfect, and something can occasionally get slightly misread or miscategorized, especially in a fast-moving discussion with multiple people talking over each other. A quick review before distribution catches these before they become the official record everyone's expected to work from.

The human reviewing the output catches it during the review step that should happen before minutes get distributed. Since the generated summary and MOM are meant to be checked before they're treated as final, a missed or misclassified topic gets corrected there instead of quietly becoming the permanent, inaccurate record of what happened.

This is exactly why skipping the review step is a mistake worth avoiding, no matter how reliable the output has seemed on previous meetings.

Meeting AI does not replace these roles. It cannot direct the session, make calls, or ensure tasks are actually concluded. It also does not ensure progress gets made just because it listed a task that needs to be completed. Instead, Meeting AI takes away the burden of recording and rehashing what was done, which allows the PM or facilitator to direct the discussion and leads to being productive. The tool does not control the discussion; the burden of side tasks competing for attention is controlled.

Meeting AI is currently available through limited early access. The skill is being rolled out to selected teams, with access currently available through the iFieldSmart AI tool.

This approach allows teams to continue refining the experience with real construction workflows before making broader access available.

Through iFieldSmart's website, where teams can register for early access ahead of general availability. Signing up secures a place in line for onboarding as capacity opens, not an immediate activation the moment the form's submitted. Expect a phased rollout here, consistent with how other skills have opened up: early access first, wider availability following as the tool's refined with real usage.

Email AI, most directly, since both sit under the same coordination and communication intelligence umbrella and often touch the same underlying project decisions and action items. Submittal tracking, RFI data, and drawing records also connect indirectly, since meeting discussions frequently reference exactly this kind of project information.

A coordination meeting discussing a specific RFI, for instance, touches the same underlying data an RFI-focused skill would also work with directly.

Native recording or upload from Zoom, Teams, and Google Meet, automatic processing into summaries and full Minutes of Meeting, and a searchable chat interface for questioning any single meeting or an entire project's meeting room at once. The focus of the tool is the shift of some of the steps of record-notes-search-follow up to upload-process-ask-retrieve.

As the tool grows beyond its initial release, the details of certain features and levels of integration may continue to change, so it’s advisable to check the latest documentation when general availability comes to see what details here may have changed.